通过数据分片训练概率电路，实现分布式环境下高效扩展。
Scaling Probabilistic Circuits via Data Partitioning
- 将数据分片后递归训练概率电路，连接联邦学习与概率建模。
- 在多类大规模数据集上实现电路模型的可扩展训练，速度更快。
- 统一处理水平、垂直及混合联邦学习，适用于多种分类任务。
概率电路（PCs）能够以可高效计算的方式学习一组随机变量的联合分布，并执行各种概率查询。尽管其可计算性使PCs可扩展至贝叶斯网络等不可计算模型之外，但在更大、更真实的现实数据集上实现PCs的训练与推理仍具挑战性。为此，我们展示了如何通过递归划分分布式数据集，在多台机器上学习概率电路，揭示了PCs与联邦学习（FL）之间的深层联系。由此提出联邦电路（FCs）——一种新颖且灵活的联邦学习框架，（1）可在分布式学习环境中扩展概率电路；（2）加快电路训练速度；（3）首次将水平、垂直和混合联邦学习统一于同一框架中，将联邦学习重新定义为分布式数据集上的密度估计问题。我们在多个大规模数据集上验证了FCs扩展概率电路的能力，并在多种分类任务中展示了其在处理各类联邦学习场景中的通用性。
原文摘要 · Abstract (English)
Probabilistic circuits (PCs) enable us to learn joint distributions over a set of random variables and to perform various probabilistic queries in a tractable fashion. Though the tractability property allows PCs to scale beyond non-tractable models such as Bayesian Networks, scaling training and inference of PCs to larger, real-world datasets remains challenging. To remedy the situation, we show how PCs can be learned across multiple machines by recursively partitioning a distributed dataset, thereby unveiling a deep connection between PCs and federated learning (FL). This leads to federated circuits (FCs) -- a novel and flexible federated learning (FL) framework that (1) allows one to scale PCs on distributed learning environments (2) train PCs faster and (3) unifies for the first time horizontal, vertical, and hybrid FL in one framework by re-framing FL as a density estimation problem over distributed datasets. We demonstrate FC's capability to scale PCs on various large-scale datasets. Also, we show FC's versatility in handling horizontal, vertical, and hybrid FL within a unified framework on multiple classification tasks.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。